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Depth-Anything is a monocular depth estimation foundation model that produces dense per-pixel depth maps from a single RGB image. It is built on a DINOv2 Vision Transformer encoder backbone and trained on 62 million unlabeled images using a teacher-student pseudo-labeling framework, enabling robust generalization across diverse scenes without task-specific training. The model outputs both relative depth maps, which capture the ordering of scene points, and metric depth maps with real-world units after fine-tuning on datasets like NYUv2 or KITTI. The project distinguishes itself through its ab
This project is a monocular depth estimation model and computer vision framework designed to calculate absolute distance and scale from single images. It functions as a metric depth estimator that generates high-resolution depth maps without requiring camera-specific focal length metadata. The system utilizes a vision transformer architecture for feature extraction and zero-shot inference to produce metric-scale depth predictions. It includes specialized components for sharp-boundary depth refinement to maintain high-frequency edge details and prevent blurriness at object boundaries. The rep
Depth-Anything-3 is a collection of core model implementations for depth prediction, multi-view geometry estimation, and RGB-D spatial pipelines. It includes a monocular depth estimation model for predicting depth maps from single images or video, and a 3D Gaussian splatting generator that predicts parameters to synthesize high-fidelity novel views of a scene. The project provides a multi-view geometry estimator for calculating spatially consistent depth and camera poses across synchronized visual inputs. It also functions as a visual SLAM enhancement tool designed to reduce drift and improve
MiDaS is a PyTorch computer vision library and monocular depth estimation model designed to predict scene depth from single images. It functions as a scene depth predictor that computes distance maps to determine object proximity to the camera. The project enables zero-shot depth transfer, allowing the model to be applied to new datasets or environments without additional training data. It focuses on relative depth regression to predict scale-invariant depth maps. The library includes a real-time depth visualizer for capturing live camera feeds and displaying corresponding depth maps. It als
Depth-Anything-V2 is a computer vision foundation model designed for general-purpose spatial understanding and depth perception. It functions as a monocular depth estimation model that predicts relative and absolute depth maps from single images or video sequences.
The main features of depthanything/depth-anything-v2 are: Monocular Depth Estimators, Computer Vision Models, Depth Estimation, Metric, Relative, Temporal Video, Video Depth Frameworks, Relative-to-Metric Depth Scaling.
Open-source alternatives to depthanything/depth-anything-v2 include: liheyoung/depth-anything — Depth-Anything is a monocular depth estimation foundation model that produces dense per-pixel depth maps from a single… bytedance-seed/depth-anything-3 — Depth-Anything-3 is a collection of core model implementations for depth prediction, multi-view geometry estimation,… apple/ml-depth-pro — This project is a monocular depth estimation model and computer vision framework designed to calculate absolute… isl-org/midas — MiDaS is a PyTorch computer vision library and monocular depth estimation model designed to predict scene depth from… vt-vl-lab/3d-photo-inpainting — This project is an RGB-D image inpainting tool and framework for 3D photo reconstruction. It transforms single 2D… baowenbo/dain — DAIN is a video frame synthesis engine and AI video upsampling tool designed to increase video playback smoothness. It…